The current state of the art in online search engines, generally involving word-based or phrase-based searching, is reasonably advanced in its ability to retrieve documents (e.g., web pages, images, files, etc.) that are responsive to the terms of a query, typically searched and retrieved using keywords contained in the query. While such search engines typically return results that accurately correspond to the search terms (keywords) of the query, the search results may not reflect the user's underlying interests and goals.
Additionally, using such keyword searching, too many search results and an insufficient quality of the search results from may be returned by the search engine, creating several problems. First, such a large number of results are returned that the user cannot review all of the results in a reasonable period of time, or within the time allocated for review by the user. Second, because of the large number of results, search providers typically return results which have been ranked according to some criteria applied by the search provider, such as by the Google page ranking system based upon the number of links to a selected web page (or website) provided by third parties as an indicator of the importance of the selected web page (or website).
In many cases, the ranked search results are distorted, both by being over-inclusive in the results returned, and by distortion of the rankings of the results. For example, keyword searching can be “gamed”, with websites or documents including various keywords simply to be included in ranked results, resulting in over-inclusion of these otherwise irrelevant websites or documents in the search results. Also for example, keywords can be purchased from a search provider, often through a daily bidding process, resulting in distorted search result rankings, with the highest rankings in the search results going to the highest bidder.
Not only does this result in overall inaccuracy of the results returned, but also it increases the amount of data which must be transmitted to the user, much of which is irrelevant and which serves to obscure or bury the relevant data sought by the user of the search engine, essentially hiding the relevant “needle” in the irrelevant “haystack”. The increased amount of transmitted data also tends to require larger databases for data storage, larger system server and memory requirements, and further serves to overload various network and Internet systems and effectively increase the overall search time.
In addition, this type of Internet searching may also be under-inclusive, missing the most relevant information which may not utilize the particular keyword and failing to return relevant results.
These problems of over-inclusiveness, under-inclusiveness and distorted rankings creates additional problems in many industries. For example, in Internet-based employment searching, resumes are often created using typical search keywords, so that an applicant's name and resume will be in the search results returned in a keyword search by a potential employer. In addition, many employment websites are aggregators of employment postings. The end result is that a company may receive thousands to hundreds of thousands of resumes for job postings which cannot be effectively winnowed or reduced through additional keyword searching, and again means that the recruiter (such as a potential employer) cannot review all of the resume results in a reasonable period of time, or within the time allocated for review by the employer (e.g., the time interval between receipt of the search results and when the applicant would be expected to interview and start employment). For example, so many resumes may be received which would require hundreds of person-hours to review, while only several (e.g., 2-3) person-hours may be allocated to review the submitted resumes, making a thorough review effectively impossible.
As a further result, search results returned in these over-inclusive situations do not provide fully actionable information. For example, when faced with a thousand resumes for a job posting, a potential employer may simply pick several which are literally at the top of the stack, such as a stack of resumes ordered based on the time each was received (if at all), or may pick a candidate based on an uneducated referral (such as from a relative), potentially overlooking many more qualified candidates. In addition, the end result for a job applicant may be multiple and undesired inquiries from potential employers offering jobs for which the applicant has no interest. These poor search results have associated costs, both in the time and effort spent searching, and in employee turnover.
A need remains, therefore, for a system and method for personalization of Internet-based search results and search result ranking in a search engine. A need also remains for a system and method for customizable filtering of Internet-based search results and search result ranking in a search engine. Such a search engine should provide an alternative to keyword searching, and should produce actionable results, such as returning a reasonable number of search results of high quality, that are directly relevant to the personalized search and without being under-inclusive, and further which can be thoroughly reviewed by the user within the user's time allocation. Such a search engine should also result in a decrease in the amount of data required to be stored and decrease the corresponding size of the resulting databases, further serving to decrease the amount of data required to be transmitted and reduce the system load. In addition, such a search engine system and method should incorporate time sensitivity in the personalized search results and provide corresponding user notifications.